Gemini Live sends no usageMetadata and no turnComplete for
gemini-3.5-transcribe-live sessions, so realtime spend logged as 0.0.
Attach estimated usage to the input_audio_transcription.completed event
using Google's published billing estimate (25 audio tokens/sec of input,
175 text tokens/min of output) derived from the streamed pcm16 audio
duration, gated to audio_transcription-mode models so conversational
Live models keep billing through usageMetadata. Also capture that usage
in the provider_config backend path so realtime cost calculation sees it.
Adds a Gemini audio transcription config that maps /v1/audio/transcriptions
onto the Interactions API (speaker attribution and word timestamps land on
the OpenAI verbose_json shape), registers both models with published pricing,
routes text-only Live sessions to TEXT responseModalities so
gemini-3.5-transcribe-live sessions survive, and makes the token-priced
transcription cost path provider-aware instead of hardcoding OpenAI.
For non-Anthropic models served over /v1/messages, the outer wrapper recomputes
cost over the adapter-translated Anthropic response dict. That dict dropped every
web search usage signal, so the recompute overwrote the correct cost breakdown
with a token-only one: x-litellm-response-cost-tool-usage read 0.0 and
x-litellm-response-cost-original excluded the search cost, while the total kept it.
The adapter now maps web search request counts (from Usage.server_tool_use or
Gemini's prompt_tokens_details) into usage.server_tool_use.web_search_requests,
matching the Anthropic API shape, and the Gemini web search cost calculator falls
back to server_tool_use when prompt_tokens_details carries no count. The shared
get_web_search_requests helper is now public since five modules consume it.
Resolves LIT-6288
get_fireworks_session_id fell back to litellm_trace_id when no session id was
given. That id is generated per request (uuid4 when absent), so x-session-affinity
carried a different value every time and Fireworks prompt caching never hit;
cached_tokens stayed 0 across identical prompts.
The None path the original change described was effectively unreachable because
of it. Drop the fallback so affinity comes only from an id the caller actually
supplied: litellm_session_id, session_id, or metadata.session_id.
Callers who were relying on a trace id for affinity can pass litellm_session_id
instead, which is stable across the requests they want grouped.
Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com>
Gemini API Maps-grounded prompts were billed as web search and Vertex AI Maps-grounded prompts were not billed at all. Classify grounding metadata per candidate into web search vs Maps requests, carry a distinct google_maps_grounding_requests usage counter through non-streaming and streaming paths, and price it via the new google_maps_grounding_cost_per_query cost map key with per-query and per-prompt defaults keyed off web_search_billing_unit. Fixes#35906
Live probes against every gemini_native_audio model on both providers show
setup accepts a valid prebuilt voice and 1007s only unknown voice names, so
the strip predicate rested on a false premise and silently discarded the
client's voice on AI Studio native-audio sessions
Vertex AI Live accepts a speechConfig on setup for native-audio models, so
stripping it silently discarded the voice a client asked for. Confirmed against
a live BidiGenerateContent session on gemini-live-2.5-flash-native-audio and on
gemini-live-2.5-flash-preview-native-audio-09-2025: both return setupComplete
with speechConfig present.
The strip stays in place for Google AI Studio, which was never verified to
accept it, via an overridable predicate matching the existing
_include_function_response_id pattern. The responseModalities TEXT to AUDIO
coercion is unchanged, since Vertex does reject TEXT on these models.
